One of the main unresolved problems in data mining is related with the treatment of data that is inherently sequential. Algorithms for the inference of association rules that manipulate sequential data have been proposed and used to some extent but are ineffective, in some cases, because too many candidate rules are extracted and filtering the relevant ones is difficult and inefficient. In this work, we present a method and algorithm for the inference of sequential association rules that uses context-free grammars to guide the discovery process, in order to filter, in an efficient and effective way, the associations discovered by the algorithm.